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1 Data Visualization Report Student’s name Professor Course Date

2 Table of content Contents Table of content ..................................................................................................................................... 2 Introduction ........................................................................................................................................... 2 Discussion .............................................................................................................................................. 3 The Daily Visitors by Outlet ................................................................................................................ 3 The daily visitor plot for each outlet over time .................................................................................. 4 The daily visits plot for each outlet .................................................................................................... 5 Daily visitor count for each outlet ...................................................................................................... 6 The plot of Daily Visitors at NFH vs RAN ............................................................................................ 7 The plot of daily visitor distribution at NFH ....................................................................................... 8 The plot of outliers - high visitor count outlets .................................................................................. 9 A plot of Average Visitors ................................................................................................................. 10 Interactive bar plot .......................................................................................................................... 11 An interactive scatter plots .............................................................................................................. 12 Critical review ...................................................................................................................................... 12 Conclusions .......................................................................................................................................... 13

3 Introduction The graphical depiction of data and information is known as data visualization. It uses charts, graphs, and other visual aids to express information effectively. Data visualization aids in discovering trends, patterns, and outliers in data that would be difficult to detect from raw data alone. In today's data-driven decision-making processes, effective data visualization is important. It can assist enterprises in making better decisions by giving insights into large amounts of data. Data visualization may be applied in various industries, including finance, healthcare, education, and marketing. Data visualization entails choosing the best approach and ensuring the display correctly depicts the data. The graphic representation selected should make the facts easy to interpret. Discussion The Daily Visitors by Outlet

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4 The Daily Visitors by Outlet plot may detect patterns and trends in data over time. It offers an overview of how each outlet performs in terms of daily visitor traffic and can assist in identifying outliers or anomalous behaviour. In this scenario, the plot demonstrates that numerous sources, such as RAN, DMN, and EYS, have consistently high daily visitor traffic. On the other side, other sources, such as AGN, ZMY, and YMQ, have persistently low daily visitor traffic. Also, the figure demonstrates some seasonality in the data, with higher daily visitor traffic in the summer and decreased traffic in the winter. Overall, the Daily Visitors by Outlet map is a great tool for assessing broad patterns and trends in the data and detecting outliers or unexpected behaviour. The daily visitor plot for each outlet over time The daily visitor plot for each outlet over time is a useful visualization for understanding the pattern and seasonality of visitor traffic for each outlet. We can determine any patterns or trends over time by charting the daily visits for each outlet on the y-axis and the date on the x-axis.

5 This visualization highlights numerous crucial data insights. For starters, it aids in identifying any seasonality in visitor traffic. For example, specific stores have increased traffic in the summer or holiday season. Second, it can assist us in identifying any unexpected or outlier behaviour for a certain source, such as a rapid reduction or surge in visitor numbers. Finally, we can use this visualization to compare visitor traffic across different outlets and find any similar or different trends. Overall, this visualization is valuable in offering a full view of visitor traffic across multiple outlets across time, which may help discover possible areas for development or optimization . The daily visits plot for each outlet The daily visits plot for each outlet depicts the total number of visitors to each outlet during the whole data period. This visualization aids in determining which outlets are the busiest and least crowded. Examining the plot, we can observe that a few outlets have

6 significantly larger daily visitor counts than the others, and these are the outlets in which the corporation is most interested. This visualization is beneficial for determining the general trend of visits to each outlet and comprehending the dispersion of daily visitor numbers. It can also display notable variations in visitor counts for any location over time. This data can be used to discover any abnormalities or patterns that need further investigation. Overall, the daily visitor plot for each outlet gives a thorough picture of visitor counts over time, which may be beneficial for recognizing patterns and making educated decisions regarding resource allocation and marketing tactics. Daily visitor count for each outlet The daily visitor count for each outlet Total Visitors by Outlet displays the total number of visits for each outlet during the dataset. This image illustrates each outlet's total popularity and relevance, which is valuable for spotting high, medium, and low-volume outlets. We can see which outlets have the greatest traffic and which have the least by looking at the overall

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7 number of visits. This data helps us decide which channels to prioritize and spend more resources to boost traffic and income. The plot of Daily Visitors at NFH vs RAN The scatter plot of Daily Visitors at NFH vs. RAN is the link between the number of visitors daily at two different outlets (NFH and RAN). This image is significant because it enables us to determine whether there is a correlation or link between the number of visits at these two locations. We can observe whether there is a pattern in the data by looking at this plot, such as if the number of visitors at one outlet impacts the number of visitors at the other. This data can assist us in making educated decisions regarding marketing, staffing, and other business-improvement tactics. This plot was chosen because it allows us to visualize the link between the number of visits at two locations over time. We may learn about future business possibilities and problems by investigating this relationship. For example, if we find a high positive

8 correlation between the number of visitors at NFH and RAN, we would explore cross- promoting these two venues to drive greater traffic to both. Instead, if there is no link between the two outlets, we may concentrate on tactics to increase the performance of each outlet separately. The plot of daily visitor distribution at NFH The plot of daily visitor distribution at NFH is significant in illustrating the pattern of daily visitor distribution at the outlet. We can examine how frequently specific ranges of visits occur by producing a histogram of the number of visitors daily. This figure can tell whether the distribution is normal or skewed, as well as the data dispersion. It can also indicate any data outliers. We utilized this figure to understand the daily visiting pattern at NFH better and to see whether there were any uncommon or outlier days. It may also be used to see if the distribution of daily visitors at different locations follows a similar trend.

9 The plot of outliers - high visitor count outlets The plot of outliers - high visitor count outlets- identifies outlets with considerably different daily visits than the bulk. This graphic depicts the outliers in the data set and their visitor numbers. We can see from this plot that some outlets have a substantially greater daily visitor count than most. For example, DMN, RAN, and RFY have significantly greater daily visitor counts than the other sources. On the other hand, other outlets have a substantially lower daily visitor count than the rest. For example, AGN, ZYT, and YMQ have significantly lower daily visitor counts than the other outlets. This graphic was used to detect outliers in the data set and to obtain insight into the visitor count distribution of the outlets. This information can help you understand the various

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10 reasons influencing the visitor counts of the outlets and make educated decisions based on the data. A plot of Average Visitors The average visitor plot shows how many visitors each outlet receives daily. This image is significant since it shows the average popularity of each outlet. We can compare the popularity of different outlets and determine the outlets with the greatest and lowest average number of visits by glancing at the graph. This data may be utilized to make resource allocation decisions, such as staffing or marketing activities. For example, outlets with a high average visitor count may require more personnel to give better customer care, whereas outlets with a low average visitor count may require greater marketing efforts to attract more consumers. As a result, this depiction is valuable for evaluating the general popularity of each channel and can assist in making decisions.

11 Interactive bar plot The interactive bar plot displays each outlet's daily average number of visits. The user can hover over each bar to see the average number. This visualization is handy since it summarizes the outlets with the greatest and lowest average visitor numbers. According to the bar plot, certain outlets have more average daily visits than others. For example, outlet DMN has about 1,800 daily visits, and outlet AGN receives only 13 daily visitors. This data may be used to make decisions and allocate resources. For example, if the corporation wishes to commit more resources to outlets with larger visitor quantities, it may rapidly discover the outlets with the highest averages using this visualization.

12 An interactive scatter plots An interactive scatter plot can help you visualize the relationship between two variables. In this example, we may plot the total money made by each outlet versus the number of visitors. It allows us to spot any patterns or trends between these variables. The scatter plot can demonstrate whether or not there is a positive association between the number of visitors and income earned, implying that outlets with a larger number of visitors make more money. On the other hand, it can disclose whether or not there is an important link between these variables, implying that other factors may impact each channel's money. The plot's interactivity allows us to hover over each point and identify the outlet it represents and the numbers for the number of visitors and income earned. It might be beneficial for spotting any outliers or strange trends in the data. Critical review This lesson taught me the value of data visualization in presenting complex data intelligibly. I used several visualizations in this course, such as line plots, bar graphs, scatter

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13 plots, and box plots, to efficiently explore and evaluate the dataset. Each visualization was chosen depending on the data's nature and the precise insights we sought. Best practices were displayed in the selection and production of each visualization, including the use of suitable chart types, the use of clear and succinct labels, and the inclusion of an acceptable title and legend for each plot. Furthermore, adding color and data labels made the charts more accessible and intelligible. This lesson taught me the value of data visualization in presenting complex data intelligibly. I used several visualizations in this course, such as line plots, bar graphs, scatter plots, and box plots, to efficiently explore and evaluate the dataset. Each visualization was chosen depending on the data's nature and the precise insights we sought. Best practices were displayed in the selection and production of each visualization, including the use of suitable chart types, the use of clear and succinct labels, and the inclusion of an acceptable title and legend for each plot. Furthermore, adding color and data labels made the charts more accessible and intelligible. Conclusions Based on the analysis and visualizations of the data, the following conclusions can be drawn:  The outlets with the highest daily visitor count are RAN, DMN, RFY, and EEC, while the outlets with the lowest are AGN, YMQ, and ZMY.  The outlet with the highest total visitor count is IZX, followed by XSB, DMN, and BMF.  NFH has the highest average daily visitors, followed by DSA and EYS.  The daily visitors at NFH and RAN have a positive correlation, indicating that one outlet's popularity may impact the other's popularity.

14  The distribution of daily visitors at NFH is approximately normal, with most days having between 60-110 visitors.  Several high visitors count outliers exist among the outlets, including DMN, RFY, RAN, and EEC. Overall, these findings can provide valuable insights for optimizing each outlet's resource allocation and marketing strategies.